**3. ICPS medical software**

*Type 2 Diabetes - From Pathophysiology to Cyber Systems*

(HFr), and their ratio LFr/HFr [12–13, 28].

can be displayed on MHM or MST.

**2.3 Analysis and interpretation**

exercise capacity.

**2.2 Data flow**

from heart rate and measuring maximal activity energy expenditure (aEEmax) during graded exercise. The VO2max calculation model uses multiple linear regression with data on age, sex, height, percent body fat, aEEmax, and the slope between HR and physical activity as in [29]. CVFM is self-adapting (self-learning) from the daily incoming data and assesses changes of VO2max, exercise capacity, and heart rate reserve. We adopted the Critical Power model from [30] which is defined as the maximal sustainable aerobic power not causing "fatigue" to measure

Ad 3. For CVS modeling ODI uses its Cardio-vegetative Stress Model (CVSM) which calculates the state variables (SV's) measuring functioning of the autonomous nervous system and estimating imbalance between sympathetic vs. parasympathetic activity. The time domain measure is the standard deviation of R-R intervals (SDNN) and the frequency domain power spectrum indicators are the low frequency spectral power of HRV (LFr), the high frequency spectral power of HRV

The usage of ICPS ORI FIT-MET™ is centered around data flowing in and out of the system. ICPS works with a wearable Sensor System (SS) to provide input data for the process models to arrive at metrics regarding CMD, CVD, CVS. The heart rate and physical activity energy expenditure related input data come from a wearable wristwatch-type fitness tracker like Garmin's smart watch. The body composition and hydration status related input data come from Garmin's Index scale. Alternatively, ODI developed its own fitness tracker, the sensor belt (SB) [7], and the BC-HS-A stand up scale [8, 9]. During regular use, ICPS updates every day the SV's and creates metrics allowing for trend prediction. The input and result data

ODI's proposition is that MC modeling can provide special insight into physiological/ pathophysiological processes. MC modeling gives the expected direction of change of a variable in the future i.e. by connecting the data points and drawing a trajectory of the predicted changes. The benefit is that instead of comparing the user's data against a group average, the individualized modeling and data trajectory creation allows for self-comparison to historical data, capturing individual characteristics and facilitating individualized interventions. The MC models are generating metrics and trajectories allowing for tracking progress and facilitating dynamic behavioral changes. The undeniable advantage of modern portable electronics is that they can provide the resources and powerful data for self-healing in a non-judgmental way. The self-explaining context of SV's have the potential to raise self-awareness and draw attention to risk reduction and individual responsibility in the fight against modifiable noncommunicative disease processes. The derived metrics provided by ICPS have the potential to give the opportunity for education and learning about risks for health, development of new skills to fight risks, building motivation, as well as measuring self-efficacy in the fight against modifiable risks. The same ICPS metrics can be used by a personal trainer/primary provider for teaching and guiding needed changes of lifestyle or behavior. Importantly, it must be emphasized that the most important tool in our armamentarium to enhance insulin sensitivity and along with-it fat burning is endurance training [31] and it works even if no

**412**

weight loss is achieved.

#### **3.1 Description of the process models**

Inspired by the call for person-centered, outcome-driven treatment as a new paradigm for treatment of type 2 diabetes in primary care [14] we present here our vision of how MC type approaches could significantly help goals set forth by the academic authors in [14]. Target points for outcome in [14] are ASCD, CKD, and HF. For a practicing primary physician, it is desirable to offer non-invasive monitoring for patients in their natural environment not just for early detection of deterioration but also to improve patients' handling of rising issues with appropriate behaviors.

Here we offer a preview about ICPS as a Medical Software and show how we can construct trajectories of SV's quasi real time in five domains of disease processes: 1. CMD and Metabolic Syndrome (MS), 2. Atherosclerotic Cardiovascular Disease (ASCD), 3. CVS and Autonomic Dysfunction (AD), 4. Chronic Anemia due to CKD, and 5. Heart failure (HF). It appears natural to extend the use of ICPS non-Medical Software with the areas of Chronic Anemia due to CKD and Heart failure. The respective process models are the following:

Ad1. The MC model for CMD and MS remains the same Cardiometabolic Function Model (CMFM) as in ICPS ORI FIT-MET™. Response to the therapies of metabolic syndrome could be tracked and compared with baseline for de novo lipogenesis DNL, Fat vs. Carbohydrate Oxidation, and 24 h non-protein respiratory quotient. These metrics can supply valuable feedback in terms of ongoing diet and exercise habits with implications to spur needed change.

Ad 2. For ASCD we want to extend CVFM. In the modeling of the maximum oxygen uptake capacity (VO2max) we also want to consider modeling oxygen delivery which depends on hemoglobin concentration (Hb), total hemoglobin mass due to chronic anemia of CKD and cardiac output. For modeling of oxygen delivery and oxygen consumption we use the model equations in [32]. For process modeling of hemoglobin concentration, total hemoglobin mass, and cardiac output see also Ad 4. and 5.

Ad 3. For CVS modeling ODI uses CVSM. For quantifying AD, the rationale is that there are strong associations between central adiposity (which is a marker of insulin resistance) and autonomic dysfunction [33] and there is an increased sympathetic system activity in metabolic syndrome [34]. We plan on using promising markers beyond SDNN, LFr, and HFr to recognize AD such as heart rate recovery time [33]. For the prediction of sudden cardiac death, we want to also use the correlation dimension of R-R intervals D2 [35].

Ad 4. We want to build a modeling platform for Chronic Anemia due to CKD. The main rationale is that anemia is a recognized risk factor for cardiovascular disease [36]. This is potentially important because iron deficiency anemia, if corrected, may in fact improve endothelial function and potentially improve morbidity and mortality [36]. Not surprisingly, anemia and insulin resistance and type 2 diabetes are interlinked [37] through various inflammatory processes which play crucial roles in the development of insulin resistance. There is also an inverse correlation between iron levels and HbA1c [38]. The reasons for this include kidney complications, neuropathy, and malabsorption occurring in the setting of advanced DM2. The elevated blood sugar will, over time, damage small blood vessels in the kidneys leading also to CKD. The erythropoietin production by the kidney goes down and along with it the production of red blood cells by bone marrow. Several studies show that diabetics with reduced renal function are more likely to end up with iron deficiency anemia than those without reduced

*Type 2 Diabetes - From Pathophysiology to Cyber Systems*

renal function [38]. The significance of monitoring hemoglobin concentration and mass is that it determines exercise performance, surgical outcome [39], and impacts heart failure [40].

The self-adapting process model of anemia of CKD (SAM-AC) will predict future hemoglobin concentration and total hemoglobin mass based on non-invasively measured hemoglobin concentration (Hb), extracellular water (ECW), and intracellular water (ICW). The ECW and ICW comes from ICPS ORI FIT-MET™. For capturing and predicting dynamics of changes of hemoglobin concentration ( *Hbk* ) and hemoglobin mass ( )*<sup>k</sup> tHbmass* for day *k* we use the following process models (Eqs. (1–3 and 5)) and measurement model (Eq. (4)). Hb concentration measurement comes from a non-invasive hemoglobin concentration measuring device like in [41, 42]. Data of daily *a posteriori* estimates of ( ) *ECWk* <sup>+</sup> and ( ) *<sup>k</sup> ICW* <sup>+</sup> will come from ODI's ICPS ORI FIT-MET™. We assume that 7.4% of the total body water constitutes the plasma volume (PV). Further we assume that the plasma albumin concentration is semi-constant, and it is not changing as rapidly as ECW and ICW, then the following formula could be used for plasma volume as in Eq. (1):

$$PV\_k^{(\*)} = \left(EC\mathcal{W}\_k^{(\*)} + IC\mathcal{W}\_k^{(\*)}\right).\text{\(0.074; \tag{1}$$

The initial hemoglobin mass is calculated as 0 00 *tHbmass Hb PV* = . . The process equation for *a priori* (denoted as ( − )) hemoglobin mass on day *k* is in Eq. (2):

$$\text{tfH}b\text{mass}\_{k}^{(-)} = \text{Hb}\_{k-1}^{(\*)} \cdot \text{PV}\_{k}^{(\*)} + \mathfrak{u}\_{k};\tag{2}$$

The process equation for *a priori* (denoted as ( − )) hemoglobin concentration prediction is in Eq. (3):

$$Hb\_k^{(-)} = \frac{tHbmass\_k^{(-)}}{PV\_k^{(\*)}} + w\_k;\tag{3}$$

The measurement model with the measured hemoglobin concentration *Hbk* on day *k* is in Eq. (4):

$$Hb\_k = Hb\_k^{(-)} + \upsilon\_k;\tag{4}$$

**415**

**3.2 Data flow**

*Integrated Cyber-Physical System to Support Early Diagnosis and Prevention of Prediabetes…*

Lagrange multipliers and the Kalman filter with constraint as in (1) for minimizing error in Eqs. (4) in the estimation/ prediction process. The modeling calculation allows recognition of the significant deviation between measured and expected/ predicted values for Hb. A sudden significant change (determined by statistical testing) can be either from sudden change of total water content or change of

Ad5. Non-invasive monitoring of Heart failure (HF) for flair ups and avoidance of admissions or readmission to the hospital has been the core element of cost reduction programs [43]. Frequently used strategy to reduce readmission rate includes behavior related recommendations: 1) Take medications as prescribed, 2) Monitor daily weights, 3) Stay active every day, 4) Follow low salt, fluid restricted diet, and 5) Recognize symptoms of heart failure and how to respond early. Our proposition regarding this issue is that the recommendations 2–5 could be helped with an ICPS Medical Software with appropriate sensor device. The bioimpedance measurement of ECW and ICW comes handy because of convenience and safety. As mentioned in Section 1. Introduction ODI has created BC-HS-A and gathered significant experience with this technology and improved the modeling and measurement technique by individualization of the measurement models [7–10]. The personalization can make bioelectric measurements extremely useful not just under physiological but also under pathophysiological conditions. For measuring cardiac function, we want to use Impedance Cardiography (ICG). Regarding accuracy of ICG it is stated in [44] that when ICG is used for intra-subject measurements with same device for continuous monitoring of cardiac stroke output the performance and accuracy is better and surpasses those of inter-subject measurements. The usage of ICG has been verified in clinical studies [45]. ICG can provide calculated SV's such as Cardiac Output, Cardiac Index, and other hemodynamic parameters. Our self-adapting process model of HF (SAM-HF) will capture metrics of HF in terms of cardiac output, weight, ECW, ICW, VO2max, heart rate variability, Hb, oxygen delivery, and other hemodynamic indices by ICG. All these metrics can be integrated to an individual-

ized HF score improving interpretation and facilitating clinical use.

the sensor system (SS) is processed by the ICPS Medical Software.

visualized in **Table 1**. entitled, "ICPS Medical Software".

The flow of data in and out of ICPS as a Medical Software is like that one of ICPS

ODI wants to use the SV's and metrics of change from baseline and determine the physiological reserve of the variables on a continuum for preventive purposes before reaching significant disease, decompensation, or death. This concept is

The increasing risk of major morbidity/ mortality is represented by a thickening red stripe as the physiological reserve capacity diminishes. The tapering arrow in blue symbolizes diminishing reserve capacity and represents the target

ORI FIT-MET™. The sensor system consists of the following parts: 1. The heart rate and physical activity energy expenditure related data come from a wearable wristwatch-type fitness tracker or ODI's Sensor Belt (SB). 2. The body composition and hydration status related data come from our specialized Body Composition and Hydration Status Analyzer (BC-HS-A) and stand up scale [7–10]. 3. For noninvasive hemoglobin concentration measurement, one could use a smart phone app [41] or measuring device with lap top connection [42]. 4. Regarding Impedance Cardiography (ICG), development kits are available [46]. ODI has the vision to develop its own hemoglobin concentration measuring sensor device and ICG device and integrate all these sensors via Bluetooth wireless communication to BC-HS-A which serves also as a base unit communicating directly to ICPS Medical Software. The incoming data from

*DOI: http://dx.doi.org/10.5772/intechopen.94232*

hemoglobin mass change or both.

The process equation for *a posteriori* (denoted as ( + )) hemoglobin concentration is in Eq. (3):

$$Hb\_k^{(+)} = Hb\_k^{(-)} + K\_k \cdot \left(Hb\_k - Hb\_k^{(-)}\right);\tag{5}$$

Here *Kk* symbolizes the Kalman gain provided by the Kalman filter. The random terms *uk* , *wk* , and *<sup>k</sup> v* represent errors and are assumed to be normally distributed with expectancy value and initial value of zero and estimated variance values with assumed non-zero initial value which is updated throughout the time of observation by the Kalman filter algorithm. Applying the Kalman filter guarantees minimum variance for errors. We use the maximum information entropy principle with

#### *Integrated Cyber-Physical System to Support Early Diagnosis and Prevention of Prediabetes… DOI: http://dx.doi.org/10.5772/intechopen.94232*

Lagrange multipliers and the Kalman filter with constraint as in (1) for minimizing error in Eqs. (4) in the estimation/ prediction process. The modeling calculation allows recognition of the significant deviation between measured and expected/ predicted values for Hb. A sudden significant change (determined by statistical testing) can be either from sudden change of total water content or change of hemoglobin mass change or both.

Ad5. Non-invasive monitoring of Heart failure (HF) for flair ups and avoidance of admissions or readmission to the hospital has been the core element of cost reduction programs [43]. Frequently used strategy to reduce readmission rate includes behavior related recommendations: 1) Take medications as prescribed, 2) Monitor daily weights, 3) Stay active every day, 4) Follow low salt, fluid restricted diet, and 5) Recognize symptoms of heart failure and how to respond early. Our proposition regarding this issue is that the recommendations 2–5 could be helped with an ICPS Medical Software with appropriate sensor device. The bioimpedance measurement of ECW and ICW comes handy because of convenience and safety. As mentioned in Section 1. Introduction ODI has created BC-HS-A and gathered significant experience with this technology and improved the modeling and measurement technique by individualization of the measurement models [7–10]. The personalization can make bioelectric measurements extremely useful not just under physiological but also under pathophysiological conditions. For measuring cardiac function, we want to use Impedance Cardiography (ICG). Regarding accuracy of ICG it is stated in [44] that when ICG is used for intra-subject measurements with same device for continuous monitoring of cardiac stroke output the performance and accuracy is better and surpasses those of inter-subject measurements. The usage of ICG has been verified in clinical studies [45]. ICG can provide calculated SV's such as Cardiac Output, Cardiac Index, and other hemodynamic parameters. Our self-adapting process model of HF (SAM-HF) will capture metrics of HF in terms of cardiac output, weight, ECW, ICW, VO2max, heart rate variability, Hb, oxygen delivery, and other hemodynamic indices by ICG. All these metrics can be integrated to an individualized HF score improving interpretation and facilitating clinical use.

### **3.2 Data flow**

*Type 2 Diabetes - From Pathophysiology to Cyber Systems*

impacts heart failure [40].

prediction is in Eq. (3):

day *k* is in Eq. (4):

tion is in Eq. (3):

renal function [38]. The significance of monitoring hemoglobin concentration and mass is that it determines exercise performance, surgical outcome [39], and

The self-adapting process model of anemia of CKD (SAM-AC) will predict future hemoglobin concentration and total hemoglobin mass based on non-invasively measured hemoglobin concentration (Hb), extracellular water (ECW), and intracellular water (ICW). The ECW and ICW comes from ICPS ORI FIT-MET™. For capturing and predicting dynamics of changes of hemoglobin concentration ( *Hbk* ) and hemoglobin mass ( )*<sup>k</sup> tHbmass* for day *k* we use the following process models (Eqs. (1–3 and 5)) and measurement model (Eq. (4)). Hb concentration measurement comes from a non-invasive hemoglobin concentration measuring

will come from ODI's ICPS ORI FIT-MET™. We assume that 7.4% of the total body water constitutes the plasma volume (PV). Further we assume that the plasma albumin concentration is semi-constant, and it is not changing as rapidly as ECW and ICW, then the following formula could be used for plasma volume as in Eq. (1):

( ) ( ) ( ) ( ) . 0.074; *PV ECW ICW k kk*

The initial hemoglobin mass is calculated as 0 00 *tHbmass Hb PV* = . . The process

( ) ( ) ( )

The process equation for *a priori* (denoted as ( − )) hemoglobin concentration

( ) ( )

−

*k k k tHbmass Hb <sup>w</sup> PV*

The measurement model with the measured hemoglobin concentration *Hbk* on

( ) ; *Hb Hb v k kk*

The process equation for *a posteriori* (denoted as ( + )) hemoglobin concentra-

Here *Kk* symbolizes the Kalman gain provided by the Kalman filter. The random terms *uk* , *wk* , and *<sup>k</sup> v* represent errors and are assumed to be normally distributed with expectancy value and initial value of zero and estimated variance values with assumed non-zero initial value which is updated throughout the time of observation by the Kalman filter algorithm. Applying the Kalman filter guarantees minimum variance for errors. We use the maximum information entropy principle with

( ) ; *<sup>k</sup>*

−

equation for *a priori* (denoted as ( − )) hemoglobin mass on day *k* is in Eq. (2):

+ ++ = + (1)

<sup>1</sup> . ; *k k kk tHbmass Hb PV u* − ++ = <sup>−</sup> + (2)

<sup>+</sup> = + (3)

<sup>−</sup> = + (4)

( ) ( ) ( ) *Hb Hb K Hb Hb k kkkk* . ; ( ) + − <sup>−</sup> =+ − (5)

<sup>+</sup> and ( ) *<sup>k</sup> ICW* <sup>+</sup>

device like in [41, 42]. Data of daily *a posteriori* estimates of ( ) *ECWk*

**414**

The flow of data in and out of ICPS as a Medical Software is like that one of ICPS ORI FIT-MET™. The sensor system consists of the following parts: 1. The heart rate and physical activity energy expenditure related data come from a wearable wristwatch-type fitness tracker or ODI's Sensor Belt (SB). 2. The body composition and hydration status related data come from our specialized Body Composition and Hydration Status Analyzer (BC-HS-A) and stand up scale [7–10]. 3. For noninvasive hemoglobin concentration measurement, one could use a smart phone app [41] or measuring device with lap top connection [42]. 4. Regarding Impedance Cardiography (ICG), development kits are available [46]. ODI has the vision to develop its own hemoglobin concentration measuring sensor device and ICG device and integrate all these sensors via Bluetooth wireless communication to BC-HS-A which serves also as a base unit communicating directly to ICPS Medical Software. The incoming data from the sensor system (SS) is processed by the ICPS Medical Software.

ODI wants to use the SV's and metrics of change from baseline and determine the physiological reserve of the variables on a continuum for preventive purposes before reaching significant disease, decompensation, or death. This concept is visualized in **Table 1**. entitled, "ICPS Medical Software".

The increasing risk of major morbidity/ mortality is represented by a thickening red stripe as the physiological reserve capacity diminishes. The tapering arrow in blue symbolizes diminishing reserve capacity and represents the target


#### **Table 1.**

*ICPS medical software.*

for improvement. The diagram shows also major tools for how vanishing physiological reserve in each health category could be improved and potentially help restore health. ODI's leap ahead innovation is *to use ICPS to collect highly impactful data, compress them into MC models, and determine and predict the model parameters which become the target for optimization of physiological functioning to reduce risk for morbidity/mortality.*

**Table 1** also shows the MC models and the respective SV's which are used to calculate metrics of change and Risk Scores (see second column from the left in **Table 1**). The possible intervention types for each MC models are listed as well (see third column from the left in **Table 1**). Handling recognized major morbidities and crisis is shown in the rightmost column of **Table 1**.

#### **3.3 Analysis and interpretation**

MC modeling can provide special insight into physiological or pathophysiological processes alike, giving the expected direction of change of a data point in the future i.e. connecting the dots or putting them on a model trajectory and explaining the changes. The benefit is that instead of comparing the user's data against a group average, the individualized modeling and data trajectory creation allows for individualized interventions and support goals of person-centered, outcome-driven

**417**

**Table 2.**

*Integrated Cyber-Physical System to Support Early Diagnosis and Prevention of Prediabetes…*

treatment as outlined by [14]. The MC models with trajectories and predictions allow for quantifying progress and for providing metrics for dynamic behavioral interventions supported by smart portable devices. The self-explaining context of SV's (metrics) have the potential to raise self-awareness and draw attention to risk reduction and individual responsibility in the fight against modifiable noncommunicative disease processes. The derived metrics provided by the MC models of ICPS have the potential to give the opportunity for education and learning about risks for health, development of new skills to fight risks, building motivation, as well as measuring self-efficacy in the fight against modifiable risks. The same ICPS metrics can be used by primary provider for teaching and guiding needed changes of lifestyle or behavior. A specialized sensor system such the Sensor Belt might provide

important information to help manage also emerging emergency situations.

when the focus is on non-fatal outcomes because of specific causes [47].

Use of ICPS allows for machine learning to optimize the MC models to fit the best to the available data and enhance the accuracy and predictive value of the derived metrics and help maximize the control over results. **Table 2**. Entitled, "The Pathways to Maximize Control" gives a conceptual summary of how the collected data can be analyzed by ICPS and how the derived metrics can facilitate interventions across lifespan. In the future, an ICPS as a Medical Software could allow for Cyber-therapy i.e. to become a medical device allowing for diagnosis and therapy. Under such a scenario, an automated self-adaptive model will assess SV's at baseline and throughout pathophysiological changes. It is foreseen that autonomous computer-generated optimal control could be enabled to maximize improvements and realize individualized "precision" medicine with strict supervision by a health professional [10]. When the disease processes(es) enter crisis stage in a

> **Pathophysiological range possible interventions**

Self-healing with behavior modification and using ICPS

Interventions by health care provider/ team to guide therapy also using information from

Autonomous computergenerated optimal control to maximize results and realize individualized "precision" medicine with strict supervision by health professional

ICPS

**Major morbidity with crisis**

Optimized learned behaviors to secure survival until rescue

Lifesaving interventions by rescue team using data also from ICPS

Autonomous machine directed therapies which can be overruled by physician

It is ODI's vision to develop its point-based risk-scoring system to summarize the relationship between SV's and the risk of the occurrence of a major morbidity event and have a Risk Score related to the five domains of functioning (in leftmost column of **Table 1**). The Risk Score calculation systems are popular among physicians and can facilitate evidence based clinical decision making [47]. The proposed Risk Score may permit effective risk stratification and assessing patient prognosis

*DOI: http://dx.doi.org/10.5772/intechopen.94232*

**Intervention type Physiological** 

Self Care Self-education,

Managed Care/ therapy using information from ICPS medical device

Cyber-therapy (ICPS medical device allowing for diagnosis and therapy)

*The pathways to maximize control.*

**range methods of choice**

learning, following guidelines for healthy lifestyle

Teaching/ learning how to improve health with use of ICPS

Machine Learning of healthy baseline functioning

#### *Integrated Cyber-Physical System to Support Early Diagnosis and Prevention of Prediabetes… DOI: http://dx.doi.org/10.5772/intechopen.94232*

treatment as outlined by [14]. The MC models with trajectories and predictions allow for quantifying progress and for providing metrics for dynamic behavioral interventions supported by smart portable devices. The self-explaining context of SV's (metrics) have the potential to raise self-awareness and draw attention to risk reduction and individual responsibility in the fight against modifiable noncommunicative disease processes. The derived metrics provided by the MC models of ICPS have the potential to give the opportunity for education and learning about risks for health, development of new skills to fight risks, building motivation, as well as measuring self-efficacy in the fight against modifiable risks. The same ICPS metrics can be used by primary provider for teaching and guiding needed changes of lifestyle or behavior. A specialized sensor system such the Sensor Belt might provide important information to help manage also emerging emergency situations.

It is ODI's vision to develop its point-based risk-scoring system to summarize the relationship between SV's and the risk of the occurrence of a major morbidity event and have a Risk Score related to the five domains of functioning (in leftmost column of **Table 1**). The Risk Score calculation systems are popular among physicians and can facilitate evidence based clinical decision making [47]. The proposed Risk Score may permit effective risk stratification and assessing patient prognosis when the focus is on non-fatal outcomes because of specific causes [47].

Use of ICPS allows for machine learning to optimize the MC models to fit the best to the available data and enhance the accuracy and predictive value of the derived metrics and help maximize the control over results. **Table 2**. Entitled, "The Pathways to Maximize Control" gives a conceptual summary of how the collected data can be analyzed by ICPS and how the derived metrics can facilitate interventions across lifespan. In the future, an ICPS as a Medical Software could allow for Cyber-therapy i.e. to become a medical device allowing for diagnosis and therapy. Under such a scenario, an automated self-adaptive model will assess SV's at baseline and throughout pathophysiological changes. It is foreseen that autonomous computer-generated optimal control could be enabled to maximize improvements and realize individualized "precision" medicine with strict supervision by a health professional [10]. When the disease processes(es) enter crisis stage in a


#### **Table 2.**

*The pathways to maximize control.*

*Type 2 Diabetes - From Pathophysiology to Cyber Systems*

**interventions**

W, L, F, WCF, R-, Rw-ratio, Fox/Cox, 24hRQ, DNL, Metrics & Risk Scoring

VO2max, Exercise Capacity, heart rate reserve Metrics &

HR, SDNN, LFr, HFr, Metrics

Hb concentration Hb mass Metrics & Risk Scoring

Cardiac Output Cardiac Index Metrics & Risk Scoring

Risk Scoring

& Risk Scoring

**Pathophysiological range metrics and possible** 

Behavior modification/ Lifestyle Change/ Dynamic behavioral modification with

ICPS

ICPS

ICPS

Cardiopulmonary exercise/ Dynamic exercises planning with ICPS

Care by cardiologist/ Dynamic planned interactions supported with metrics from ICPS

Care by provider and supplementation of needed factor(s), Automatic alert by

Care by provider and following guidelines Automatic alert by

**Major morbidity with** 

Organ failure and crisis

Metabolic catastrophe with need for urgent intervention

Cardiorespiratory failure with need for urgent intervention

Nerve exhaustion/ pending sudden cardiac death needing urgent intervention

Symptomatic anemia with need for urgent intervention

Symptomatic Heart Failure with need for urgent intervention

**crisis**

**Domains of health and MC models**

Morbidity mortality Physiological reserve

Cardio-Metabolic Health, CMFM & Metabolic Syndrome

Cardiovascular Health CVFM & ASCD

Cardiovegetative Stress CVSM & AD

Chronic Anemia due to CKD & SAM-AC

Heart Function& SAM-HF

**Table 1.**

for improvement. The diagram shows also major tools for how vanishing physiological reserve in each health category could be improved and potentially help restore health. ODI's leap ahead innovation is *to use ICPS to collect highly impactful data, compress them into MC models, and determine and predict the model parameters which become the target for optimization of physiological functioning to reduce risk for* 

**Table 1** also shows the MC models and the respective SV's which are used to calculate metrics of change and Risk Scores (see second column from the left in **Table 1**). The possible intervention types for each MC models are listed as well (see third column from the left in **Table 1**). Handling recognized major morbidities and

MC modeling can provide special insight into physiological or pathophysiological processes alike, giving the expected direction of change of a data point in the future i.e. connecting the dots or putting them on a model trajectory and explaining the changes. The benefit is that instead of comparing the user's data against a group average, the individualized modeling and data trajectory creation allows for individualized interventions and support goals of person-centered, outcome-driven

**416**

*morbidity/mortality.*

*ICPS medical software.*

crisis is shown in the rightmost column of **Table 1**.

**3.3 Analysis and interpretation**

home environment one can foresee the possibility of remote autonomous machine directed therapies which can be overruled by a physician.

## **4. Discussion**

From person-centered, outcomes-driven treatment point of view of type 2 diabetes the innovation of the Integrated Cyber-Physical System Medical Software is that it can capture metrics in 5 intertwined domains of physiological or pathophysiological functioning in the user's natural environment non-invasively. Data can be obtained in the metabolic, cardiovascular, cardio-vegetative, hematological (circulating hemoglobin mass), and cardiac functioning health domains.

The ICPS non-Medical Software (ORI FIT-MET™) realizes already now the observation of metrics in the metabolic, cardiovascular, and cardio-vegetative health domains with preventative purpose. Input data regarding heart rate and physical activity energy expenditure come from a watch-type fitness tracker such as Garmin smart watch and from serially measured body composition and hydration data such as the Garmin Index scale. ICPS allows a quasi-real time monitoring of metrics of functioning for the user and personal trainer/primary provider and allowing for self-healing and directed lifestyle interventions. Analysis, prediction, and planning for change can be performed either at home or optionally in the personal trainer/primary provider's office through a web app and display of results on the user's smartphone. Unique to our effort is that our suggested state variables are connected to risks of morbidity and mortality and allow risk assessment continuously over a lifespan, raising self-awareness, enhancing motivation, and underscoring self-responsibility to reduce modifiable risks as much as possible. Metabolic health goals, like improved metabolic flexibility, improved insulin resistance along with greater lean mass and optimized fat versus carbohydrate burning can be approached with the help of ICPS ORI FIT-MET™ through feedback of information from a personalized self-adaptive mathematical model of the energy metabolism. ICPS can also help optimize cardiorespiratory fitness level by providing feedback of indirectly estimated maximum oxygen uptake from heart rate and measuring maximal activity energy expenditure. Knowing the fitness level by VO2max can help set the optimal training loads for endurance training leading to improved resilience, fat oxidation and insulin sensitivity [31]. Cardio-vegetative stress level is estimated by time domain and frequency domain analysis providing metrics for the overall activation of the sympathetic system which is a non-specific marker of vegetative state and should not be interpreted without appropriate clinical context, but it has significant prognostic value for overall health status and change of it.

This chapter outlined the scope of an ICPS Medical Software which still must be built. The significance of this outlined plan is to show that with already existing technology, goals of [14] can be supported. The exciting perspective is that ICPS Medical Software or a similar device will undoubtedly allow for big data collection and data mining and thereby provide the foundation for truly individualized "precision" medicine. ICPS in its fully developed form could provide information about primary interlinked pathological processes of whole-body oxidative stress, inflammation, and insulin resistance. Multiple observational studies have demonstrated already that these primary pathological processes are intricately linked to metabolic syndrome, atherosclerotic disease, sympathetic nerve activation, anemia of chronic kidney disease, and heart failure. ICPS with its state variables and derived metrics & Risk Scoring can potentially give the opportunity to calculate risks of non-fatal major morbidity outcomes in the 5 studied domains and define clear targets for specific individualized interventions. Even treatments of complications of heart

**419**

**5. Conclusion**

*Integrated Cyber-Physical System to Support Early Diagnosis and Prevention of Prediabetes…*

failure could be feasible at home, potentially avoiding frequent readmissions to the

Before making ICPS non-Medical Software (ORI FIT-MET™) available to the public several important problems need to be addressed. The technical hurdle is to create a scalable versatile mobile and cloud computing platform for ICPS which can potentially be used with a variety of mobile health products on the market. While ODI wants to make ICPS potentially usable with various mobile health products, this effort may be stifled because of a lack of interoperability of various fitness devices and because data are stored in "data silos," preventing users and health professionals from getting an integrated view of health and fitness data [48]. The current practice is for third-party developers to retrieve the data via an open API with permission of the owner of the API and the user. The key risk and challenge are to make users' data accessible for cloud computing systems like ICPS. A short list of other problems to be overcome is as follows: data privacy and security, to create a marketable product which is only a fitness device at this stage of development and remains a non-medical device category, creating tools for easy calorie intake counting, creating tools for visceral fat mass measurement, and educating future

To create an ICPS Medical Software would pose even more challenges. The needed sensor hardware components must be developed and interfaced with ICPS. The main reason for ODI developing its own hardware for ICPS Medical Software is to avoid the 3rd party API issues and to guarantee top security for data flow with the latest and possibly most up to date technology. The other reason to have self-developed hardware is to have information regarding errors of measurements. ODI uses intensively the Kalman filter technology which works best if the standard deviation of the error of the measuring instrument is known. This allows "tuning" the Kalman filter to have the best performance. The seemingly daunting proposition for ODI to build its own hardware is mitigated by the fact that major electronic device companies offer their sensors with fully developed reference designs for hardware and software. This should help to build the needed sensors such as the watch-type fitness tracker, the Sensor Belt for ECG and waist circumference monitoring, the body composition and hydration status measuring standup scale [9], the hemoglobin concentration measuring finger sensor, and the Impedance Cardiography which could be also designed as a wearable sensor belt for continuous use or it could be integrated into the afore mentioned stand up scale for one point in time use. Phase I clinical study is needed to verify accuracy and certify analytical performance and safety. Phase II study is desirable to demonstrate utility and create user guide for patients and physicians. Reimbursement for the use of ICPS is also an issue as insurance companies may want

users and also physicians about the complex science behind ICPS.

to have proof that ICPS is able to save cost and improve clinical outcome.

and improvement of risk factors for prevention, treating type 2 diabetes and complications. A value-based health delivery system holds potential to incentivize participants to improve their lifestyle, especially if insurance companies would honor participants with a discount on the premiums for those who were successful in lowering their cardiometabolic, cardiovascular, and cardio-vegetative risk.

After proper consenting, secondary analysis of metabolic data could help not only clinical research and pursuing goals of Global Health [11], but also insurance companies to calculate costs and potentially reimburse the treatment/self-treatment

In conclusion, ICPS can serve as an appropriate quasi real-time tool to monitor and optimally adjust modifiable risk factors. The trends/trajectories of metabolic values

*DOI: http://dx.doi.org/10.5772/intechopen.94232*

hospital.

*Integrated Cyber-Physical System to Support Early Diagnosis and Prevention of Prediabetes… DOI: http://dx.doi.org/10.5772/intechopen.94232*

failure could be feasible at home, potentially avoiding frequent readmissions to the hospital.

Before making ICPS non-Medical Software (ORI FIT-MET™) available to the public several important problems need to be addressed. The technical hurdle is to create a scalable versatile mobile and cloud computing platform for ICPS which can potentially be used with a variety of mobile health products on the market. While ODI wants to make ICPS potentially usable with various mobile health products, this effort may be stifled because of a lack of interoperability of various fitness devices and because data are stored in "data silos," preventing users and health professionals from getting an integrated view of health and fitness data [48]. The current practice is for third-party developers to retrieve the data via an open API with permission of the owner of the API and the user. The key risk and challenge are to make users' data accessible for cloud computing systems like ICPS. A short list of other problems to be overcome is as follows: data privacy and security, to create a marketable product which is only a fitness device at this stage of development and remains a non-medical device category, creating tools for easy calorie intake counting, creating tools for visceral fat mass measurement, and educating future users and also physicians about the complex science behind ICPS.

To create an ICPS Medical Software would pose even more challenges. The needed sensor hardware components must be developed and interfaced with ICPS. The main reason for ODI developing its own hardware for ICPS Medical Software is to avoid the 3rd party API issues and to guarantee top security for data flow with the latest and possibly most up to date technology. The other reason to have self-developed hardware is to have information regarding errors of measurements. ODI uses intensively the Kalman filter technology which works best if the standard deviation of the error of the measuring instrument is known. This allows "tuning" the Kalman filter to have the best performance. The seemingly daunting proposition for ODI to build its own hardware is mitigated by the fact that major electronic device companies offer their sensors with fully developed reference designs for hardware and software. This should help to build the needed sensors such as the watch-type fitness tracker, the Sensor Belt for ECG and waist circumference monitoring, the body composition and hydration status measuring standup scale [9], the hemoglobin concentration measuring finger sensor, and the Impedance Cardiography which could be also designed as a wearable sensor belt for continuous use or it could be integrated into the afore mentioned stand up scale for one point in time use. Phase I clinical study is needed to verify accuracy and certify analytical performance and safety. Phase II study is desirable to demonstrate utility and create user guide for patients and physicians. Reimbursement for the use of ICPS is also an issue as insurance companies may want to have proof that ICPS is able to save cost and improve clinical outcome.

After proper consenting, secondary analysis of metabolic data could help not only clinical research and pursuing goals of Global Health [11], but also insurance companies to calculate costs and potentially reimburse the treatment/self-treatment and improvement of risk factors for prevention, treating type 2 diabetes and complications. A value-based health delivery system holds potential to incentivize participants to improve their lifestyle, especially if insurance companies would honor participants with a discount on the premiums for those who were successful in lowering their cardiometabolic, cardiovascular, and cardio-vegetative risk.

### **5. Conclusion**

In conclusion, ICPS can serve as an appropriate quasi real-time tool to monitor and optimally adjust modifiable risk factors. The trends/trajectories of metabolic values

*Type 2 Diabetes - From Pathophysiology to Cyber Systems*

**4. Discussion**

directed therapies which can be overruled by a physician.

home environment one can foresee the possibility of remote autonomous machine

From person-centered, outcomes-driven treatment point of view of type 2 diabetes the innovation of the Integrated Cyber-Physical System Medical Software is that it can capture metrics in 5 intertwined domains of physiological or pathophysiological functioning in the user's natural environment non-invasively. Data can be obtained in the metabolic, cardiovascular, cardio-vegetative, hematological

The ICPS non-Medical Software (ORI FIT-MET™) realizes already now the observation of metrics in the metabolic, cardiovascular, and cardio-vegetative health domains with preventative purpose. Input data regarding heart rate and physical activity energy expenditure come from a watch-type fitness tracker such as Garmin smart watch and from serially measured body composition and hydration data such as the Garmin Index scale. ICPS allows a quasi-real time monitoring of metrics of functioning for the user and personal trainer/primary provider and allowing for self-healing and directed lifestyle interventions. Analysis, prediction, and planning for change can be performed either at home or optionally in the personal trainer/primary provider's office through a web app and display of results on the user's smartphone. Unique to our effort is that our suggested state variables are connected to risks of morbidity and mortality and allow risk assessment continuously over a lifespan, raising self-awareness, enhancing motivation, and underscoring self-responsibility to reduce modifiable risks as much as possible. Metabolic health goals, like improved metabolic flexibility, improved insulin resistance along with greater lean mass and optimized fat versus carbohydrate burning can be approached with the help of ICPS ORI FIT-MET™ through feedback of information from a personalized self-adaptive mathematical model of the energy metabolism. ICPS can also help optimize cardiorespiratory fitness level by providing feedback of indirectly estimated maximum oxygen uptake from heart rate and measuring maximal activity energy expenditure. Knowing the fitness level by VO2max can help set the optimal training loads for endurance training leading to improved resilience, fat oxidation and insulin sensitivity [31]. Cardio-vegetative stress level is estimated by time domain and frequency domain analysis providing metrics for the overall activation of the sympathetic system which is a non-specific marker of vegetative state and should not be interpreted without appropriate clinical context, but it has

(circulating hemoglobin mass), and cardiac functioning health domains.

significant prognostic value for overall health status and change of it.

This chapter outlined the scope of an ICPS Medical Software which still must be built. The significance of this outlined plan is to show that with already existing technology, goals of [14] can be supported. The exciting perspective is that ICPS Medical Software or a similar device will undoubtedly allow for big data collection and data mining and thereby provide the foundation for truly individualized "precision" medicine. ICPS in its fully developed form could provide information about primary interlinked pathological processes of whole-body oxidative stress, inflammation, and insulin resistance. Multiple observational studies have demonstrated already that these primary pathological processes are intricately linked to metabolic syndrome, atherosclerotic disease, sympathetic nerve activation, anemia of chronic kidney disease, and heart failure. ICPS with its state variables and derived metrics & Risk Scoring can potentially give the opportunity to calculate risks of non-fatal major morbidity outcomes in the 5 studied domains and define clear targets for specific individualized interventions. Even treatments of complications of heart

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calculated by the mathematical models can serve as tools, allowing for planning and executing dynamic changes of behavior for optimization and control of these values. All-encompassing Risk Scores calculated by the mathematical models can serve as outcome measures to be tracked by the user and personal trainer/primary provider to prevent and fight burdens of type 2 diabetes and optimize lifestyle quasi real-time.
